Falls affect millions of people each year and result in significant injuries, particularly among the elderly. In fact, it has been estimated that falls are one of the top three causes of death in elderly people. A fall is defined as a sudden, uncontrolled and unintentional downward displacement of the body to the ground, followed by an impact, after which the body stays down on the ground.
A personal emergency response system (PERS) is a system in which help for a user can be assured. By means of Personal Help Buttons (PHBs) the user can push the button to summon help in an emergency. A majority of calls are because the user has fallen. Also, if the user suffers a severe fall (for example by which they get confused or even worse if they are knocked unconscious), the user might be unable to push the button, which might mean that help doesn't arrive for a significant period of time, particularly if the user lives alone. The consequences of a fall can become more severe if the user stays lying for a long time.
Fall detection systems are also available that process the output of one or more movement sensors to determine if the user has suffered a fall. Most existing body-worn fall detection systems make use of an accelerometer (usually an accelerometer that measures acceleration in three dimensions) and they are configured to infer the occurrence of a fall by processing the time series generated by the accelerometer. Some fall detection systems can also include an air pressure sensor, for example as described in WO 2004/114245, for measuring the height, height change or absolute altitude of the fall detection system. On detecting a fall, an alarm is triggered by the fall detection system.
Some fall detection systems are designed to be worn as a pendant around the neck of the user, whereas others are designed to be worn on or at the torso (e.g. waist, on a waist belt or in a pocket) or on the limbs of the user, for example at the wrist.
A lot of effort is being put into providing robust classification methods or processing algorithms for detecting falls accurately. In general, a fall detector tests on features like impact, orientation, orientation change, height change, vertical velocity, and alike. Reliable detection results when the set of computed values for these features is different for falls than for other movements that are not a fall. The algorithm can compare the detected features with predetermined threshold values and/or classification patterns to determine if a fall event has occurred.
The reliability of the classification method can be visualized by a receiver operating characteristic (ROC) curve in which the detection probability is plotted against the false alarm rate. FIG. 1 shows such ROC-curve which represents the average performance of the algorithm across many users tested over a long time period. The optimal trade-off between detected falls and false alarms (the ‘operating point’) depends on several factors, such as customer/user satisfaction and economic factors. A high rate of false alarms is costly for the service centre and annoying to the customer (user), whereas diminishing the amount of false alarms may lead to missed falls which can be extremely troublesome or harmful to the customer (user). It is the aim of the fall detection algorithm designer to create an algorithm with an operating point that reaches the upper-left corner of the ROC curve as closely as possible. However, the precise operating point can depend on the mentioned external conditions and preferences.
In general, people with a low fall risk are more active and may generate more movements in daily life that appear to the fall detection algorithm as falls. As a consequence, the amount of false alarms may be higher than average for this ‘low fall risk’ group, while the amount of actual falls is lower than average. FIGS. 2 and 3 show exemplary relationships between false alarm rate/true falls and the fall risk/activity level respectively. The curves may take other shapes. For example, the false alarm rate may have a maximum half way in both graphs.